Night sleep in patients with vegetative state

Sleep in Disorders of Consciousness: A Brief Overview on a Still under Investigated Issue

Consciousness is a multifaceted concept, involving both wakefulness, i.e., a condition of being alert that is regulated by the brainstem, and awareness, a subjective experience of any thoughts or perception or emotion. Recently, the European Academy of Neurology has published international guidelines for a better diagnosis of coma and other disorders of consciousness (DOC) through the investigation of sleep patterns, such as slow-wave and REM, and the study of the EEG using machine learning methods and artificial intelligence. The management of sleep disorders in DOC patients is an increasingly hot topic and deserves careful diagnosis, to allow for the most accurate prognosis and the best medical treatment possible. The aim of this review was to investigate the anatomo-physiological basis of the sleep/wake cycle, as well as the main sleep patterns and sleep disorders in patients with DOC. We found that the sleep characteristics in DOC patients are still controversial. DOC patients often present a theta/delta pattern, while epileptiform activity, as well as other sleep elements, have been reported as correlating with outcomes in patients with coma and DOC. The absence of spindles, as well as REM and K-complexes of NREM sleep, have been used as poor predictors for early awakening in DOC patients, especially in UWS patients. Therefore, sleep could be considered a marker of DOC recovery, and effective treatments for sleep disorders may either indirectly or directly favor recovery of consciousness.

Sleep patterns correlates with the efficacy of tDCS on post-stroke patients with prolonged disorders of consciousness

BACKGROUND

The subclassification of prolonged disorders of consciousness (DoC) based on sleep patterns is important for the evaluation and treatment of the disease. This study evaluates the correlation between polysomnographic patterns and the efficacy of transcranial direct current stimulation (tDCS) in patients with prolonged DoC due to stroke.

METHODS

In total, 33 patients in the vegetative state (VS) with sleep cycles or without sleep cycles were randomly assigned to either active or sham tDCS groups. Polysomnography was used to monitor sleep changes before and after intervention. Additionally, clinical scale scores and electroencephalogram (EEG) analysis were performed before and after intervention to evaluate the efficacy of tDCS on the patients subclassified according to their sleep patterns.

RESULTS

The results suggest that tDCS improved the sleep structure, significantly prolonged total sleep time (TST) (95%CI: 14.387-283.527, P = 0.013) and NREM sleep stage 2 (95%CI: 3.157-246.165, P = 0.040) of the VS patients with sleep cycles. It also significantly enhanced brain function of patients with sleep cycles, which were reflected by the increased clinical scores (95%CI: 0.340-3.440, P < 0.001), the EEG powers and functional connectivity in the brain and the 6-month prognosis. Moreover, the changes in NREM sleep stage 2 had a significant positive correlation with each index of the β band.

CONCLUSION

This study reveals the importance of sleep patterns in the prognosis and treatment of prolonged DoC and provides new evidence for the efficacy of tDCS in post-stroke patients with VS patients subclassified by sleep pattern. Trial registration URL: https://www.

CLINICALTRIALS

gov . Unique identifier: NCT03809936. Registered 18 January 2019.

Behavioral Assessment of Patients With Disorders of Consciousness

SUMMARY

Brain injury resulting in coma may evolve into a prolonged disorder of consciousness, including the vegetative and minimally conscious states. Early detection of emerging consciousness has positive prognostic significance, and improvement in consciousness at any point may indicate the potential for meaningful communication and environmental control. Despite the importance of accurate assessment of consciousness, research indicates that as many as 40% of patients with a disorder of consciousness may be assessed incorrectly. Assessment of consciousness is challenging for many reasons, including the fact that consciousness cannot be measured directly but must be inferred from patterns of behavioral activity, that many patients have confounding deficits and treatments that may mask consciousness, and that patient performance may be highly variable over time. In this manuscript, we discuss strategies for optimizing patient status during assessment and review a number of structured assessment approaches that can be used. The available assessment techniques vary in their length and cost, and the expertise required to use them. Which of these approaches is most applicable to a given acute or subacute setting will vary with the volume of patients with a disorder of consciousness and the available resources. Importantly, lack of consciousness in the acute setting should not be used to justify the withdrawal of care or denial of rehabilitation services.

A Systematic Review of Sleep in Patients with Disorders of Consciousness: From Diagnosis to Prognosis

With the development of intensive care technology, the number of patients who survive acute severe brain injury has increased significantly. At present, it is difficult to diagnose the patients with disorders of consciousness (DOCs) because motor responses in these patients may be very limited and inconsistent. Electrophysiological criteria, such as event-related potentials or motor imagery, have also been studied to establish a diagnosis and prognosis based on command-following or active paradigms. However, the use of such task-based techniques in DOC patients is methodologically complex and requires careful analysis and interpretation. The present paper focuses on the analysis of sleep patterns for the evaluation of DOC and its relationships with diagnosis and prognosis outcomes. We discuss the concepts of sleep patterns in patients suffering from DOC, identification of this challenging population, and the prognostic value of sleep. The available literature on individuals in an unresponsive wakefulness syndrome (UWS) or minimally conscious state (MCS) following traumatic or nontraumatic severe brain injury is reviewed. We can distinguish patients with different levels of consciousness by studying sleep patients with DOC. Most MCS patients have sleep and wake alternations, sleep spindles and rapid eye movement (REM) sleep, while UWS patients have few EEG changes. A large number of sleep spindles and organized sleep-wake patterns predict better clinical outcomes. It is expected that this review will promote our understanding of sleep EEG in DOC.

Sleep in disorders of consciousness: diagnostic, prognostic, and therapeutic considerations

PURPOSE OF REVIEW

Sleep is important in the evaluation of patients with disorders of consciousness (DOC). However, it remains unclear whether reconstitution of sleep could enable consciousness or vice versa. Here we synthesize recent evidence on natural recovery of sleep in DOC, and sleep-promoting therapeutic interventions for recovery of consciousness.

RECENT FINDINGS

In subacute DOC, physiological sleep--wake cycles and complex sleep patterns are related to better outcomes. Moreover, structured rapid-eye-movement (REM), non-REM (NREM) stages, and presence of sleep spindles correlate with full or partial recovery. In chronic DOC, sleep organization may reflect both integrity of consciousness-supporting brain networks and engagement of those networks during wakefulness. Therapeutic strategies have integrated improvement of sleep and sleep--wake cycles in DOC patients; use of bright light stimulation or drugs enhancing sleep and/or vigilance, treatment of sleep apneas, and neuromodulatory stimulations are promising tools to promote healthy sleep architecture and wakeful recovery.

SUMMARY

Sleep features and sleep--wake cycles are important prognostic markers in subacute DOC and can provide insight into covert recovery in chronic DOC. Although large-scale studies are needed, preliminary studies in limited patients suggest that therapeutic options restoring sleep and/or sleep--wake cycles may improve cognitive function and outcomes in DOC.

Effect of Retinohypothalamic Tract Dysfunction on Melatonin Level in Patients with Chronic Disorders of Consciousness

OBJECTIVE

The aim of this study is to compare the secretion level of nocturnal melatonin and the characteristics of the peripheral part of the visual analyzer in patients with chronic disorders of consciousness (DOC).

MATERIALS AND METHODS

We studied the level of melatonin in 22 patients with chronic DOC and in 11 healthy volunteers. The fundus condition was assessed using the ophthalmoscopic method.

RESULTS

The average level of nocturnal melatonin in patients with DOC differed by 80% from the level of indole in healthy volunteers. This reveals a direct relationship between etiology, the level of consciousness, gaze fixation, coma recovery scale-revised score and the level of melatonin secretion. Examination by an ophthalmologist revealed a decrease in the macular reflex in a significant number of DOC patients, which in turn correlates negatively with the time from brain injury and positively with low values of nocturnal melatonin.

Retrospective analysis of sleep patterns in patients with chronic disorders of consciousness

Analysis of sleep patterns in patients with chronic disorders of consciousness attracts attention from the perspective of the diagnosis and prognosis of the disease as well as the treatment. Yet, the very existence of normal sleep in patients in a vegetative or minimally conscious state is still a matter of debate. This paper presents a retrospective analysis of overnight polysomnographic records of 40 patients with chronic disorders of consciousness aimed at the possibility of establishing the connection between the degree of impaired consciousness and the presence and organization of polysomnographic graphical elements, associated with stages of sleep in normal individuals. Specialized software based on expert system artificial intelligence was developed to calculate indices and parameters that characterize sleep. It was shown that a remarkably low percentage of patients have a rhythmic change in sleep patterns, what indicates the prevalence of violations of the Sleep-Wake cycle in a vegetative state and minimally conscious state. Sleep spindles were not found in records, however, the absence can originate from the limitations of polysomnographic method applied to patients with severe brain damage. A positive correlation between the rhythmic change of sleep patterns, better outcome and CRS-R scores was confirmed.

Sleep in disorders of consciousness: behavioral and polysomnographic recording

BACKGROUND

Sleep-wakefulness cycles are an essential diagnostic criterion for disorders of consciousness (DOC), differentiating prolonged DOC from coma. Specific sleep features, like the presence of sleep spindles, are an important marker for the prognosis of recovery from DOC. Based on increasing evidence for a link between sleep and neuronal plasticity, understanding sleep in DOC might facilitate the development of novel methods for rehabilitation. Yet, well-controlled studies of sleep in DOC are lacking. Here, we aimed to quantify, on a reliable evaluation basis, the distribution of behavioral and neurophysiological sleep patterns in DOC over a 24-h period while controlling for environmental factors (by recruiting a group of conscious tetraplegic patients who resided in the same hospital).

METHODS

We evaluated the distribution of sleep and wakefulness by means of polysomnography (EEG, EOG, EMG) and video recordings in 32 DOC patients (16 unresponsive wakefulness syndrome [UWS], 16 minimally conscious state [MCS]), and 10 clinical control patients with severe tetraplegia. Three independent raters scored the patients' polysomnographic recordings.

RESULTS

All but one patient (UWS) showed behavioral and electrophysiological signs of sleep. Control and MCS patients spent significantly more time in sleep during the night than during daytime, a pattern that was not evident in UWS. DOC patients (particularly UWS) exhibited less REM sleep than control patients. Forty-four percent of UWS patients and 12% of MCS patients did not have any REM sleep, while all control patients (100%) showed signs of all sleep stages and sleep spindles. Furthermore, no sleep spindles were found in 62% of UWS patients and 21% of MCS patients. In the remaining DOC patients who had spindles, their number and amplitude were significantly lower than in controls.

CONCLUSIONS

The distribution of sleep signs in DOC over 24 h differs significantly from the normal sleep-wakefulness pattern. These abnormalities of sleep in DOC are independent of external factors such as severe immobility and hospital environment.

Assessing the state of consciousness for individual patients using complex, statistical stimuli

Patients with prolonged disorders of consciousness (PDOC) are often unable to communicate their state of consciousness. Determining the latter is essential for the patient's care and prospects of recovery. Auditory stimulation in combination with neural recordings is a promising technique towards an objective assessment of conscious awareness. Here, we investigated the potential of complex, acoustic stimuli to elicit EEG responses suitable for classifying multiple subject groups, from unconscious to responding. We presented naturalistic auditory textures with unexpectedly changing statistics to human listeners. Awake, active listeners were asked to indicate the change by button press, while all other groups (awake passive, asleep, minimally conscious state (MCS), and unresponsive wakefulness syndrome (UWS)) listened passively. We quantified the evoked potential at stimulus onset and change in stimulus statistics, as well as the complexity of neural response during the change of stimulus statistics. On the group level, onset and change potentials classified patients and healthy controls successfully but failed to differentiate between the UWS and MCS groups. Conversely, the Lempel-Ziv complexity of the scalp-level potential allowed reliable differentiation between UWS and MCS even for individual subjects, when compared with the clinical assessment aligned to the EEG measurements. The accuracy appears to improve further when taking the latest available clinical diagnosis into account. In summary, EEG signal complexity during onset and changes in complex acoustic stimuli provides an objective criterion for distinguishing states of consciousness in clinical patients. These results suggest EEG-recordings as a cost-effective tool to choose appropriate treatments for non-responsive PDOC patients.

Managing disorders of consciousness: the role of electroencephalography

Disorders of consciousness (DOC) are an important but still underexplored entity in neurology. Novel electroencephalography (EEG) measures are currently being employed for improving diagnostic classification, estimating prognosis and supporting medicolegal decision-making in DOC patients. However, complex recording protocols, a confusing variety of EEG measures, and complicated analysis algorithms create roadblocks against broad application. We conducted a systematic review based on English-language studies in PubMed, Medline and Web of Science databases. The review structures the available knowledge based on EEG measures and analysis principles, and aims at promoting its translation into clinical management of DOC patients.

Clinical and advanced neurophysiology in the prognostic and diagnostic evaluation of disorders of consciousness: review of an IFCN-endorsed expert group

The analysis of spontaneous EEG activity and evoked potentialsis a cornerstone of the instrumental evaluation of patients with disorders of consciousness (DoC). Thepast few years have witnessed an unprecedented surge in EEG-related research applied to the prediction and detection of recovery of consciousness after severe brain injury,opening up the prospect that new concepts and tools may be available at the bedside. This paper provides a comprehensive, critical overview of bothconsolidated and investigational electrophysiological techniquesfor the prognostic and diagnostic assessment of DoC.We describe conventional clinical EEG approaches, then focus on evoked and event-related potentials, and finally we analyze the potential of novel research findings. In doing so, we (i) draw a distinction between acute, prolonged and chronic phases of DoC, (ii) attempt to relate both clinical and research findings to the underlying neuronal processes and (iii) discuss technical and conceptual caveats.The primary aim of this narrative review is to bridge the gap between standard and emerging electrophysiological measures for the detection and prediction of recovery of consciousness. The ultimate scope is to provide a reference and common ground for academic researchers active in the field of neurophysiology and clinicians engaged in intensive care unit and rehabilitation.

Sleep in the completely locked-in state (CLIS) in amyotrophic lateral sclerosis

Persons in the completely locked-in state (CLIS) suffering from amyotrophic lateral sclerosis (ALS) are deprived of many zeitgebers of the circadian rhythm: While cognitively intact, they are completely paralyzed, eyes mostly closed, with artificial ventilation and artificial nutrition, and social communication extremely restricted or absent. Polysomnographic recordings in eight patients in CLIS, however, revealed the presence of regular episodes of deep sleep during night time in all patients. It was also possible to distinguish an alpha-like state and a wake-like state. Classification of rapid eye movement (REM) sleep is difficult because of absent eye movements and absent muscular activity. Four out of eight patients did not show any sleep spindles. Those who have spindles also show K-complexes and thus regular phases of sleep stage 2. Thus, despite some irregularities, we found a surprisingly healthy sleep pattern in these patients.

European Academy of Neurology guideline on the diagnosis of coma and other disorders of consciousness

BACKGROUND AND PURPOSE

Patients with acquired brain injury and acute or prolonged disorders of consciousness (DoC) are challenging. Evidence to support diagnostic decisions on coma and other DoC is limited but accumulating. This guideline provides the state-of-the-art evidence regarding the diagnosis of DoC, summarizing data from bedside examination techniques, functional neuroimaging and electroencephalography (EEG).

METHODS

Sixteen members of the European Academy of Neurology (EAN) Scientific Panel on Coma and Chronic Disorders of Consciousness, representing 10 European countries, reviewed the scientific evidence for the evaluation of coma and other DoC using standard bibliographic measures. Recommendations followed the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. The guideline was endorsed by the EAN.

RESULTS

Besides a comprehensive neurological examination, the following suggestions are made: probe for voluntary eye movements using a mirror; repeat clinical assessments in the subacute and chronic setting, using the Coma Recovery Scale - Revised; use the Full Outline of Unresponsiveness score instead of the Glasgow Coma Scale in the acute setting; obtain clinical standard EEG; search for sleep patterns on EEG, particularly rapid eye movement sleep and slow-wave sleep; and, whenever feasible, consider positron emission tomography, resting state functional magnetic resonance imaging (fMRI), active fMRI or EEG paradigms and quantitative analysis of high-density EEG to complement behavioral assessment in patients without command following at the bedside.

CONCLUSIONS

Standardized clinical evaluation, EEG-based techniques and functional neuroimaging should be integrated for multimodal evaluation of patients with DoC. The state of consciousness should be classified according to the highest level revealed by any of these three approaches.

Prognostic roles of sleep electroencephalography pattern and circadian rhythm biomarkers in the recovery of consciousness in patients with coma: a prospective cohort study

OBJECTIVE

To investigate the potential prognostic value of sleep electroencephalography (EEG) pattern and serum circadian rhythm biomarkers in the recovery of consciousness in patients at the acute stage of coma.

METHODS

A prospective observational study which included 75 patients with coma was conducted. Twenty-four-hour continuous polysomnography (PSG) was performed to determine the sleep EEG pattern according to the modified Valente's Grade (mVG) that we proposed. Serum levels of melatonin and orexin-A at four consecutive time points during the PSG were examined. Patients were then followed for one month to determine their level of consciousness. Multivariate logistic regression analysis was performed to examine associations between demographics, aetiology, baseline clinical features (pupillary and corneal reflex, and neuron-specific enolase [NSE]), clinical scores (Glasgow Coma Scale-Motor Response [GCS-M], Full Outline of Unresponsiveness [FOUR] scale, Acute Physiology and Chronic Health Evaluation II [APACHE II] scale), mVG, serum circadian biomarkers, and recovery of consciousness within one month.

RESULTS

Within one month of enrolment, 34 patients regained consciousness and 36 patients remained non-conscious. Spearman rank correlation revealed a significant association between mVG and state of consciousness after one month. Significant variation in serum melatonin or orexin-A was not detected in either the conscious or non-conscious groups. Hypoxic aetiology, APACHE II, and mVG were independently associated with recovery of consciousness within one month.

CONCLUSION

Sleep EEG structure, hypoxic aetiology, and APACHE II can independently predict recovery of consciousness in patients with acute coma. Taken together, we encourage neurologists to use sleep elements to assess patients with acute coma.

Toward Improving Diagnostic Strategies in Chronic Disorders of Consciousness: An Overview on the (Re-)Emergent Role of Neurophysiology

The differential diagnosis of patients with Disorder of Consciousness (DoC), in particular in the chronic phase, is significantly difficult. Actually, about 40% of patients with unresponsive wakefulness syndrome (UWS) and the minimally conscious state (MCS) are misdiagnosed. Indeed, only advanced paraclinical approaches, including advanced EEG analyses, can allow achieving a more reliable diagnosis, that is, discovering residual traces of awareness in patients with UWS (namely, functional Locked-In Syndrome (fLIS)). These approaches aim at capturing the residual brain network models, at rest or that may be activated in response to relevant stimuli, which may be appropriate for awareness to emerge (despite their insufficiency to generate purposeful motor behaviors). For this, different brain network models have been studied in patients with DoC by using sensory stimuli (i.e., passive tasks), probing response to commands (i.e., active tasks), and during resting-state. Since it can be difficult for patients with DoC to perform even simple active tasks, this scoping review aims at summarizing the current, innovative neurophysiological examination methods in resting state/passive modality to differentiate and prognosticate patients with DoC. We conclude that the electrophysiologically-based diagnostic procedures represent an important resource for diagnosis, prognosis, and, therefore, management of patients with DoC, using advance passive and resting state paradigm analyses for the patients who lie in the “greyzones” between MCS, UWS, and fLIS.

Genetic algorithms for feature selection when classifying severe chronic disorders of consciousness

The diagnosis and prognosis of patients with severe chronic disorders of consciousness are still challenging issues and a high rate of misdiagnosis is evident. Hence, new tools are needed for an accurate diagnosis, which will also have an impact on the prognosis. In recent years, functional Magnetic Resonance Imaging (fMRI) has been gaining more and more importance when diagnosing this patient group. Especially resting state scans, i.e., an examination when the patient does not perform any task in particular, seems to be promising for these patient groups. After preprocessing the resting state fMRI data with a standard pipeline, we extracted the correlation matrices of 132 regions of interest. The aim was to find the regions of interest which contributed most to the distinction between the different patient groups and healthy controls. We performed feature selection using a genetic algorithm and a support vector machine. Moreover, we show by using only those regions of interest for classification that are most often selected by our algorithm, we get a much better performance of the classifier.

Parametric Description of EEG Profiles for Assessment of Sleep Architecture in Disorders of Consciousness

We propose a fully parametric approach to the assessment of sleep architecture, based upon the classical electroencephalographic criteria, applicable also to the recordings of patients with disorders of consciousness (DOC). Sleep spindles and slow waves are automatically detected from the matching pursuit decomposition of overnight EEG recordings. Their evolution can be presented in the form of EEG profiles, yielding a continuous description of sleep architecture, compatible with the classical criteria used in sleep staging. We propose assessment of these EEG profiles by five parameters, which can be combined by a linear classifier, assessing the quality of sleep architecture. Proposed methodology is evaluated on 59 overnight EEG recordings from 19 patients from a hospital for children with severe brain damage, in relation to their behavioral diagnosis according to the Coma Recovery Scale-Revised. Presented results indicate robustness of the proposed approach, which may serve as a valuable aid in diagnosis of DOC patients. Complete software environment for computing and presentation of EEG profiles is freely available from http://svarog.pl .

A Systematic Review and Meta-Analysis of the Relationship Between Brain Data and the Outcome in Disorders of Consciousness

A systematic search revealed 68 empirical studies of neurophysiological [EEG, event-related brain potential (ERP), fMRI, PET] variables as potential outcome predictors in patients with Disorders of Consciousness (diagnoses Unresponsive Wakefulness Syndrome [UWS] and Minimally Conscious State [MCS]). Data of 47 publications could be presented in a quantitative manner and systematically reviewed. Insufficient power and the lack of an appropriate description of patient selection each characterized about a half of all publications. In more than 80% studies, neurologists who evaluated the patients' outcomes were familiar with the results of neurophysiological tests conducted before, and may, therefore, have been influenced by this knowledge. In most subsamples of datasets, effect size significantly correlated with its standard error, indicating publication bias toward positive results. Neurophysiological data predicted the transition from UWS to MCS substantially better than they predicted the recovery of consciousness (i.e., the transition from UWS or MCS to exit-MCS). A meta-analysis was carried out for predictor groups including at least three independent studies with N > 10 per predictor per improvement criterion (i.e., transition to MCS versus recovery). Oscillatory EEG responses were the only predictor group whose effect attained significance for both improvement criteria. Other perspective variables, whose true prognostic value should be explored in future studies, are sleep spindles in the EEG and the somatosensory cortical response N20. Contrary to what could be expected on the basis of neuroscience theory, the poorest prognostic effects were shown for fMRI responses to stimulation and for the ERP component P300. The meta-analytic results should be regarded as preliminary given the presence of numerous biases in the data.

Spontaneous Recovery from Unresponsive Wakefulness Syndrome to a Minimally Conscious State: Early Structural Changes Revealed by 7-T Magnetic Resonance Imaging

Background

Determining the early changes of brain structure that occur from vegetative state/unresponsive wakefulness syndrome (VS/UWS) to a minimally conscious state (MCS) is important for developing our understanding of the processes underlying disorders of consciousness (DOC), particularly during spontaneous recovery from severe brain damage.

Objective

This study used a multi-modal neuroimaging approach to investigate early structural changes during spontaneous recovery from VS/UWS to MCS.

Methods

The Coma Recovery Scale-Revised (CRS-R) score, 24-h electroencephalography (EEG), and ultra-high field 7-T magnetic resonance imaging were used to investigate a male patient with severe brain injury when he was in VS/UWS compared to MCS. Using white matter connectometry analysis, fibers in MCS were compared with the same fibers in VS/UWS. Whole-brain analysis was used to compare all fibers showing a 10% increase in density with each other as a population.

Results

Based on connectometry analysis, the number of fibers with increased density, and the magnitude of increase in MCS compared to VS/UWS, was greatest in the area of the temporoparietal junction (TPJ), and was mostly located in the right hemisphere. These results are in accordance with the active areas observed on 24-h EEG recordings. Moreover, analysis of different fibers across the brain, showing at least a 10% increase in density, revealed that altered white matter connections with higher discriminative weights were located within or across visual-related areas, including the cuneus_R, calcarine_R, occipital_sup_R, and occipital_mid_R. Furthermore, the temporal_mid_R, which is related to the auditory cortex, showed the highest increase in connectivity to other areas. This was consistent with improvements in the visual and auditory components of the CRS-R, which were greater than other improvements.

Conclusion

These results provide evidence to support the important roles for the TPJ and the visual and auditory sensory systems in the early recovery of a patient with severe brain injury. Our findings may facilitate a much deeper understanding of the mechanisms underlying conscious-related processes and enlighten treatment strategies for patients with DOC.

Sleep in patients with disorders of consciousness characterized by means of machine learning

Sleep has been proposed to indicate preserved residual brain functioning in patients suffering from disorders of consciousness (DOC) after awakening from coma. However, a reliable characterization of sleep patterns in this clinical population continues to be challenging given severely altered brain oscillations, frequent and extended artifacts in clinical recordings and the absence of established staging criteria. In the present study, we try to address these issues and investigate the usefulness of a multivariate machine learning technique based on permutation entropy, a complexity measure. Specifically, we used long-term polysomnography (PSG), along with video recordings in day and night periods in a sample of 23 DOC; 12 patients were diagnosed as Unresponsive Wakefulness Syndrome (UWS) and 11 were diagnosed as Minimally Conscious State (MCS). Eight hour PSG recordings of healthy sleepers (N = 26) were additionally used for training and setting parameters of supervised and unsupervised model, respectively. In DOC, the supervised classification (wake, N1, N2, N3 or REM) was validated using simultaneous videos which identified periods with prolonged eye opening or eye closure.The supervised classification revealed that out of the 23 subjects, 11 patients (5 MCS and 6 UWS) yielded highly accurate classification with an average F1-score of 0.87 representing high overlap between the classifier predicting sleep (i.e. one of the 4 sleep stages) and closed eyes. Furthermore, the unsupervised approach revealed a more complex pattern of sleep-wake stages during the night period in the MCS group, as evidenced by the presence of several distinct clusters. In contrast, in UWS patients no such clustering was found. Altogether, we present a novel data-driven method, based on machine learning that can be used to gain new and unambiguous insights into sleep organization and residual brain functioning of patients with DOC.

Sleep patterns associated with the severity of impairment in a large cohort of patients with chronic disorders of consciousness

OBJECTIVE

We assessed sleep patterns in 85 patients with chronic disorders of consciousness (DOC) in order to reveal any relationship with the degree of the impairment.

METHODS

Nocturnal polysomnography (PSG) was scored in patients classified as being in an unresponsive wakefulness syndrome/vegetative state (UWS/VS; n = 49) or a minimally conscious state (MCS; n = 36) in accordance with the rules of the American Academy of Sleep Medicine. The PSG data in the two diagnostic groups were compared, and the PSG parameters associated with the degree of impairment were analysed.

RESULTS

In 19/49 UWS/VS patients, signal attenuation was the only EEG pattern detectable in sleep. Non-REM 2 (NREM2) and slow-wave sleep (SWS) (but not REM) stages were more frequent in the MCS patients. The presence of SWS was the most appropriate factor for classifying patients as UWS/VS or MCS, and the duration of SWS was the main factor that significantly correlated with revised Coma Recovery Scale scores.

CONCLUSION

The presence of NREM sleep (namely SWS) reflects better preservation of the circuitry and structures needed to sustain this stage of sleep in DOC patients.

SIGNIFICANCE

PSG is a simple and effective technique, and sleep patterns may reflect the degree of impairment in chronic DOC patients.

A Review of Resting-State Electroencephalography Analysis in Disorders of Consciousness

Recently, neuroimaging technologies have been developed as important methods for assessing the brain condition of patients with disorders of consciousness (DOC). Among these technologies, resting-state electroencephalography (EEG) recording and analysis has been widely applied by clinicians due to its relatively low cost and convenience. EEG reflects the electrical activity of the underlying neurons, and it contains information regarding neuronal population oscillations, the information flow pathway, and neural activity networks. Some features derived from EEG signal processing methods have been proposed to describe the electrical features of the brain with DOC. The computation of these features is challenging for clinicians working to comprehend the corresponding physiological meanings and then to put them into clinical applications. This paper reviews studies that analyze spontaneous EEG of DOC, with the purpose of diagnosis, prognosis, and evaluation of brain interventions. It is expected that this review will promote our understanding of the EEG characteristics in DOC.